自然和人工系统中认知过程的预测编码.
Joseph B Hopfinger1, Scott D Slotnick2
1Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA.
Cognitive neuroscience
|November 17, 2025
概括
人工智能 (AI) 模型越来越多地反映了人类的认知过程,为感知,语言和注意力提供了新的见解. 这个特别号探讨了人工智能.
科学领域:
- 认知神经科学 认知神经科学
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 人类认知机制在人工智能 (AI) 模型中的实例化越来越感兴趣.
- 人工智能模型正在结合类似于人类记忆和注意力的机制.
- 预测编码理论和生成模型在解释视觉感知和语言生成方面表现有前途.
研究的目的:
- 在人工智能和认知神经科学的交叉点提出新的数据,模型和争议.
- 探索人工智能架构 (如变压器) 如何使用类似注意力的机制来处理序列数据.
- 引入一个神经计算的语法模型,将预测过程集成到一个层次结构中.
主要方法:
- 审查和讨论新型人工智能模型及其与人类认知的关系.
- 探索变压器架构及其非马科夫的生成模型.
- 对语法处理的多层次,层次的神经计算模型的介绍.
- 实证研究听觉感知和可预测性在认知处理中的作用.
主要成果:
- 变压器架构利用非马科夫的生成模型,对顺序数据进行关键注意力类似的过程.
- 一个新的神经计算模型将预测过程集成到一个层次的语法架构中.
- 可预测的分散注意力的信息在听觉感知过程中需要较少的认知处理.
结论:
- 人工智能模型为理解人类认知和神经过程提供了强大的工具.
- 人工智能的快速进化有望在人工学习系统和大脑理论方面取得重大未来发展.
- 结合人工智能和认知神经科学的跨学科研究对于推进我们对大脑的理解至关重要.
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